Research Scientist Intern, Youth Safety (PhD)

Meta; Bellevue (WA); On-site; USD 41,000 - 55,000; Full time

Job summary

Meta invites Research Interns to join the Youth and Controls team. Work on high-impact problems at the intersection of ML, integrity, and personalization, focusing on proactive detection of harmful content for youth and age-appropriate experiences.

You will engage in evaluating models at scale, build LLM-judge and quality-eval infrastructure, and connect model improvements to teen experiences through human judgments and online experiments. Internship lengths vary throughout the year.

Qualifications

• Conduct advanced research on youth safety, integrity and content understanding.
• Develop ML models for age-understanding and content ranking.
• Improve proactive enforcement with human judgments and experiments.
• Publish research results and contribute to Meta product development.

Responsibilities

• Perform research to advance intelligent systems for youth safety.
• Develop models for age-appropriate experiences and content understanding.
• Improve efficiency, latency, and reliability of safety models in production.
• Collaborate with researchers and cross-functional partners to share plans and results.

Skills

Research fundamentals

Communication

Cross-functional collab

ML evaluating

Education

Pursuing CS/Math/Stats degree

Tools

Python

PyTorch/TensorFlow

Job description

About

Meta was built to help people connect and share, and over the last decade our tools have played a critical part in changing how people around the world communicate with one another. With over a billion people using the service and more than fifty offices around the globe, a career at Meta offers countless ways to make an impact in a fast growing organization.Meta is seeking Research Interns to join our Youth and Controls team within Facebook. Youth safety is one of the most consequential problems at Meta right now — the models you build change what millions of teenagers see every day. Our team owns age-appropriate content experiences across Facebook, including content-understanding and age-understanding models that decide what gets shown and to whom, spanning Teen Accounts, parental supervision, U13 experiences (Messenger Kids), and our most protective content tiers.More concretely, you will work on high-ambiguity, high-impact problems at the intersection of ML, integrity, and personalization: proactive detection of harmful content for youth, age-appropriate ranking and retrieval, safety-aware personalization and controls, and age-signal modeling (stated vs. predicted age reconciliation). A cross-cutting theme is evaluation at scale — building LLM-judge and quality-eval infrastructure so enforcement LLMs ship with rigorous, youth-specialized measurement, and connecting model improvements to real teen experience through human judgments and online experiments.Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.

Responsibilities

• Meta was built to help people connect and share, and over the last decade our tools have played a critical part in changing how people around the world communicate with one another.
• With over a billion people using the service and more than fifty offices around the globe, a career at Meta offers countless ways to make an impact in a fast growing organization.
• Meta is seeking Research Interns to join our Youth and Controls team within Facebook.
• Youth safety is one of the most consequential problems at Meta right now — the models you build change what millions of teenagers see every day.
• Our team owns age-appropriate content experiences across Facebook, including content-understanding and age-understanding models that decide what gets shown and to whom, spanning Teen Accounts, parental supervision, U13 experiences (Messenger Kids), and our most protective content tiers.
• More concretely, you will work on high-ambiguity, high-impact problems at the intersection of ML, integrity, and personalization: proactive detection of harmful content for youth, age-appropriate ranking and retrieval, safety-aware personalization and controls, and age-signal modeling (stated vs.
• predicted age reconciliation).
• A cross-cutting theme is evaluation at scale — building LLM-judge and quality-eval infrastructure so enforcement LLMs ship with rigorous, youth-specialized measurement, and connecting model improvements to real teen experience through human judgments and online experiments.
• Our internships are twelve (12) to sixteen (16), or twenty-four (24) weeks long and we have various start dates throughout the year.

Minimum Qualifications

• Perform research to advance the science and technology of intelligent machines, applied to youth safety and integrity
• Develop novel content-understanding, age-understanding, retrieval, ranking, and personalization models for age-appropriate experiences
• Advance proactive enforcement approaches, including reducing reliance on human review while maintaining quality and safety
• Develop training and evaluation methods using human judgments, LLM judges, and online experiments to improve safety, quality, and downstream experience
• Analyze and improve efficiency, scalability, latency, and stability of safety models in production serving
• Collaborate with researchers and cross-functional partners including communicating research plans, progress, and results
• Publish research results and contribute to research that can be applied to Meta product development Intent to return to the degree program after the completion of the internship/co-op
• Proven track record of solid research achievements as demonstrated by grants, fellowships, patents, as well as publications at leading AI conferences such as NeurIPS, ICML, ICLR, ACL, EMNLP, KDD, CVPR, SIGIR, WWW, or similar
• Prior research or project experience in one or more of the following areas: trust and safety, integrity / abuse detection, content understanding, age modeling and verification, age-appropriate recommendation, personalization, LLM-based evaluation / LLM judges, reinforcement learning, pre-training and fine-tuning of large language models, or multimodal understanding
• Demonstrated software development experience via tech internships, work experience, coding competitions, or widely used contributions in open source machine-learning repositories
• Experience working and communicating cross functionally in a fast-paced team environment, with the ability to frame real-world safety problems as rigorous ML problems and drive iterations from clear analysis
• Confirm interest in youth safety / integrity as a research domain, and comfort owning a problem end-to-end from hypothesis to shipped model